Ventricle pacing during atrial fibrillation episodes

ABSTRACT

An adaptive dual chamber pacemaker and/or cardioverter defibrillator for delivering ventricular stimulation to the heart correlated with hemodynamic performance of the heart, including a hemodynamic sensor for monitoring the hemodynamic performance of the heart, an atrial electrode and a ventricular electrode for sensing ventricular and atrial signals, and a learning module having a spiking neural network processor for learning to associate the ventricular-atrial intervals sensed by the electrodes with the hemodynamic performance sensed by the hemodynamic sensor, calculating ventricular-atrial intervals, replacing the ventricular-atrial intervals calculated from the sensed ventricular and atrial signals with the learned associated ventricular-atrial intervals, and causing delivery according to the learned associated ventricular-atrial intervals of a ventricular stimulation to the heart during atrial fibrillation episodes.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.12/601,822, filed Nov. 25, 2009, which is the National Stage ofInternational Application No. PCT/IL2006/000571, filed May 15, 2006, andtitled “Ventricle Pacing During Atrial Fibrilation Episodes,” whichclaims the benefit of U.S. Provisional Application No. 60/685,464, filedMay 27, 2005, and titled “Ventricle Pacing During Atrial FibrilationEpisodes,” where the entire contents of all of the above applicationsare incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates generally to cardiac pacing anddefibrillating and more specifically to cardiac pacing duringarrhythmias.

BACKGROUND OF THE INVENTION

Atrial fibrillation (AF) is the most common cardiac rhythm disorder andit affects an estimated 2.3 million adults in the United States, themajority of who are over the age of 65 years. Far from benign, AF canlead to stroke, tachycardia-induced cardiomyopathy, and congestive heartfailure. AF accounts for about 15% of all strokes that occur each yearin the United States. The number of patients with AF is increasingthroughout the industrialized world as the population ages. In theUnited States, the prevalence of AF is expected to grow 2.5-fold to 5.6million by 2050, and over half of those afflicted will be aged 80 orolder As the burden of this disorder grows, increased emphasis will beplaced on developing more effective ways to treat AF to reduce itsassociated morbidity and mortality (James L. Cox, MD, SurgicalManagement of Atrial Fibrillation, Medscape Cardiology, May 2005).

Implanted pacemakers and intracardiac cardioverter defibrillators (ICD)deliver therapy to patients suffering from various heart-diseases(Clinical Cardiac Pacing and Defibrillation, 2nd edition, Ellenbogen,Kay, Wilkoff, 2000). Dual chamber pacemakers pace the right ventriclewith synchrony to the sensed atrial event, with a given delay, theatrioventricular (AV) delay. Cardiac Resynchronization Therapy (CRT)devices, i.e. biventricular pacemakers, pace both ventricles, and alsosynchronize according to the sensed atrial event signal.

However, a significant proportion of patients having a dual chamberpacemaker or biventricular pacemaker suffer also from atrialfibrillation episodes, possibly long lasting. These patients will notbenefit from their dual chamber pacemaker or biventricular pacemakerduring an episode since both devices synchronize according to the sensedatrial events that are not reliable during atrial fibrillation episodes,and hence will not deliver physiologic pacing during atrial fibrillationepisodes.

In PCT publication WO0038782 a pacing system is disclosed, featuring amode switching feature and ventricular rate regularization (VRR)function capable of stabilizing or regularizing ventricular heart rateduring chronic or paroxysmal atrial tachyarrhythmia. The VRR functionaccomplishes this result by adjusting pacing rate according to thepattern of the most recent series of sensed or paced ventricular events.

In US2005187585 patent application, a method is disclosed for adaptivelysmoothing ventricular rate during atrial fibrillation (AF). According tothis method, the pacing delivered by a pacing device is switched to anon-atrial synchronized mode when AF is detected. The ventricular escapeinterval (VEI) is modulated beat-by-beat around a physiological intervalzone (PIZ), which is determined by the pre-arrhythmia ventricular rateor the output of rate responsive sensor.

Selective atrioventricular nodal (AVN) vagal stimulation (AVN-VS) hasemerged as a novel strategy for ventricular rate (VR) control in atrialfibrillation (AF). AVN-VS is delivered to the epicardial fat pad thatprojects the parasympathetic nerve fibers to the AVN. Although AVN-VSpreserves the physiological ventricular activation sequence, theresulting rate is slow and irregular. This issue is discussed in“Ventricular Rate Control by Selective Vagal Stimulation Is Superior toRhythm Regularization by Atrioventricular Nodal Ablation and PacingDuring Atrial Fibrillation”, by Shaowei Zhuang, et al, Circulation.2002; 106:1853. The authors indicate that the AVN-VS although producinga superior hemodynamic performance as compared to an ablation and pacingapproach, results in irregular ventricular contractions rate, i.e.irregular sensed R-R intervals.

P. Taggart and P. Sutton argue In “Termination of Arrhythmia byHemodynamic Unloading”, published in Cardiac Mechano-Electric Feedback &Arrhythmia, by Kohl, Sachs and Franz, Elsevier Saunders, 2005, thathemodynamic unloading should be anti arrhythmic: “ventricular or atrialunloading should tend to be protective against focal tachycardia causedby triggered activity. The foregoing theoretical predictions aresupported by several studies in different animal models in whicharrhythmia were induced by increased stretch or volume loading.”

The various methods presented above for rate regulation during atrialfibrillation episodes are based on heart rate regulation and do not takeinto account the exact cardiac cycle timings when pacing the ventriclesduring atrial fibrillation while the AVN-VS method preserves thephysiological ventricular activation sequence but produces irregularventricular contractions.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic description of an adaptive CRT employingassociation of a ventricle-atrial (VA) interval with temporal patternsof hemodynamic sensor.

FIG. 2 is a schematic description of the neural network architectureperforming biventricular pacing and VA interval association based onintracardiac electrograms (IEGMs) and hemodynamic (Impedance) sensor.

FIG. 3 is a flow diagram description of the switching between associatedventricle-atrial (VA) interval and sensed atrial event timing basedpacing according to atrial fibrillation detection.

FIG. 4 is a state diagram describing switching between associated andsensed atrial timing based pacing.

DETAILED DESCRIPTION OF THE INVENTION

In the present invention implanted hemodynamic sensors are used in a waythat reflects the hemodynamic performance of the ventricle, which islikely to be reliable also during atrial fibrillation episodes in orderto unload hemodynamic stress and suppress the arrhythmia. Thehemodynamic sensor can be used to synchronize pacing with the cardiaccycle timings and ventricular pacing in dual chamber devices orbi-ventricular pacing in CRT devices can be delivered with optimaltimings also during atrial fibrillation episodes.

In accordance with the present invention, the timing of the subsequentatrial event is predicted relative to the preceding ventricle event byassociation of temporal patterns of hemodynamic sensor with a VAinterval. By exploiting both this prediction and hemodynamic sensorsthat reflect ventricle function, a dynamic optimization of the pacingintervals can be performed for the particular patient. Such a combinedtool is useful during atrial fibrillation incidents, and hence providesa more physiologic pacing for patients suffering from atrialfibrillation episodes.

The system of the present invention employs an implanted adaptivebiventricular pacemaker (referred to also as a CRT devices), or adaptivedual chamber pacemakers or ICD devices as disclosed in PCT applicationWO 2005007075 by the inventor of the present application, the contentsof which are incorporated herein by reference. With an adaptive CRTdevice both AV delay and the VV interval vary dynamically in response tohemodynamic inputs from sensors in a closed loop system such that thestroke volume at a given heart rate is maximized online andcontinuously. This system therefore implies a feedback control. Asdescribed in FIG. 1, the system of the present invention consists of anadaptive CRT device system equipped with a neural network processor fortemporal pattern detection trained to associate the next atrial eventtiming relative to the preceding ventricle event. Spiking neural networkprocessor 12 is the learning module, working as a slave processor withmicro-controller 14. Block 16, which represents a pulse generator andoperational amplifier(s), is the analogue interface to patient heartelectrodes 18. The atrial and biventricular electrodes are implanted,namely, the right atria lead, right ventricular lead and leftventricular lead. Physiologic sensors 22 implanted at the right and leftventricles, are any sensors known in the art, for example chamberimpedance sensors, chamber pressure sensors and accelerometers. Box 24designates all the modules implanted in the patient.

In FIG. 2 to which reference is now made, the architectural aspects of aneural network that performs both biventricular pacing and atrial eventassociation with temporal patterns of hemodynamic sensors are shown.Preferably, the neural network processor of the invention is a spikingneural network processor having three layers. An input layer withtemporal decoder synchronizer 34, middle layer 36, and output layer 38.Spike controller 42 manages the neural network operations. Thearchitecture of the temporal pattern detection neural network module forthe atrial event timing relative to the preceding ventricle event, i.e.the VA interval, is similar to the one described above, having temporaldecoder synchronizer 60, middle layer 62, and output layer 64. Adetailed description of the temporal pattern detection neural network isgiven below. The three important outputs of the learning module, theneural network processor, are AV delay, VV interval and the associatedVA interval.

The adaptive CRT neural network processor architecture and its operationis described in WO 2005/007075 the contents of which are incorporatedherein by reference. The present invention provides a system and amethod that in addition to bi-ventricular pacing or right ventricularpacing replace the sensed atrial event signal during atrial arrhythmiaepisodes with an associated VA interval internally in the timingcircuits that control the ventricular and bi-ventricular pacing and aretherefore capable of continuing to pace with optimal timing theventricles during atrial fibrillation as well.

The temporal pattern detection neural network is trained using asupervised learning rule continuously supplied by the micro-controllerusing the hemodynamic sensor signal as an input from the right or leftventricle chambers. The neural network learns how to map the temporalpatterns of the hemodynamic sensor into VA intervals as long as normalsinus rhythm is detected. Whenever atrial fibrillation occurs and isdetected, the neural network associates VA interval correlated with thetemporal patterns of the hemodynamic sensor that takes over the atrialsensed event as will be explained below.

Temporal Pattern Recognition with Spiking Neurons

The first stage in the temporal pattern detection neural network, is apre-processing stage for the hemodynamic sensor temporal input signalwith a temporal synchronizer. The synchronizer excites selectively anarray of dynamic synapses of the middle layer. The middle layer hastypically 200 dynamic synapses that are arranged in a matrix having 10columns and 20 synapses in each column. The temporal synchronizer istriggered every cardiac cycle by the ventricle-sensed electrical event,i.e. ventricular IEGM, and following the trigger it excites selectivelyin a time sequence a group of dynamic synapses at each column accordingto the temporal values of the input hemodynamic sensor signal at theexcitation times. At the output layer there are typically 10 leakyintegrate-and-fires (I&F) neurons, one for each column of dynamicsynapses, that accumulate the post synaptic responses excitations (PSR)of the synapses in the middle layer. All the I&F neurons are trained tofire at the same target time which is the sensed VA interval. The outputlayer I&F neurons are trained to fire at the target time and a fuzzyaverage result using a membership function for each I&F neuron iscalculated as the final result of the neural network i.e the associatedVA interval. The fuzzy average calculation, to be further elaboratedbelow, increases the prediction accuracy of the temporal patterndetection neural network (TPDNN). The combination of fuzzy logic andneural network for control systems takes advantage of both techniques,as discussed in “Understanding Neural Networks and Fuzzy Logic: basicconcepts and applications” by Stamatios V. Kartalopoulos, Wiley—IEEEPress, August 1995 incorporated herein by reference.

Temporal Pattern Recognition Learning Scheme

In 1949 Donald Hebb proposed that ‘associative learning’ involved asimple cellular mechanism. His hypothesis known as ‘Hebb's learningrule’ claimed that “coincident activity in both cells involved iscritical for strengthening the connections (associations) between thepre-synaptic and post-synaptic neurons”. Hebb's learning rule statesthat “when an axon cell A excites cell B and repeatedly or persistentlytakes part in firing it, some growth process or metabolic change takesplace in one or both cells such that A's efficacy, as one of two cellsfiring B, is increased.” The present invention implements Hebbianlearning rules as described below.

The dynamic synapses of the pattern recognition network adapt theirinternal time delay parameter, τ_(ij), which is the time delay parameterbetween a pre-synaptic excitation by the temporal synchronizer to thesynapse post synaptic response (PSR). The synaptic time delay parameterchanges according to a supervised learning rule where the supervisedtarget time is the sensed atrial event measured from the previousventricle contraction. The Hebbian learning rule used with the spikingneurons network performing temporal pattern recognition of the presentinvention is formulated as follows:

Equation 1:τ_(ij)=τ_(ij) +η*R _(ij)  (1)where:i=0 . . . , 9 is the I&F neuron index and the column index.j=0, . . . , 19 is the synapse index within a column.η is the learning rate coefficient.R_(ij) is a function of the relative timing of the firing of the I&Fneuron, T_(i), and the target time, P_(i), as shown in Equation 2 below.

$\begin{matrix}{R_{ij} = \left\{ \begin{matrix}{{{{+ 1}\mspace{14mu}{if}\mspace{14mu} T_{i}} < P_{i}},} \\{{{{- 1}\mspace{14mu}{if}\mspace{14mu} T_{i}} > P_{i}},}\end{matrix} \right.} & (2)\end{matrix}$

In each cardiac cycle the synapses that are excited by the temporalsynchronizer starts incrementing an internal counter. When the firingtime of the I&F neuron, T_(i), occurs before the internal time delayparameter, τ_(ij), expires the synapse state is stored as a Pre Hebbstate. When the firing time occurs within a predefined short timeinterval, Δ, just after the expiration of τ_(ij), the synapse state isstored as a Hebb state. When the firing time of the I&F neuron occurslater, the synapse state is stored as a Post Hebb state. The postsynaptic response (PSR) is emitted by the synapse in the Hebb state andis accumulated on the membrane potential of the post synaptic integrateand fire neuron. Note that according to Equation 2 the relative timingsof the firing time T_(i), the target time P_(i), determines if theinternal time delay parameter is incremented or decremented

Associated VA interval and Hit Count Rate Membership Function

A hit count rate membership function, f (Ti), is defined as the numberof hits, or the hit count rate, of the I&F neuron that fires in thevicinity of the target time in a time frame of 32 cardiac cycles. Themaximal value for the function is therefore 32 when the I&F neuron firesat the correct target time every cardiac cycle, and the hit count ratemembership function is zero when the I&F neuron fires constantly out ofthe time window. For each I&F neuron the hit count rate membershipfunction is calculated and a normalized average is calculated accordingto Equation 3 belowAssociated VA=Σf(Ti)*Ti/Σf(Ti)  (3)Where Ti is the firing time of I&F neuron i and the summation is doneover all the spiking neurons of the output layer.Equation 3 defines the associated VA interval of the TPDNN using the hitcount rate membership function. The associated VA is used later toreplace the sensed VA interval during atrial fibrillation episodes.

In FIG. 3 to which reference is now made, a flow diagram describes theswitching decision the system makes between a sensed timing based pacingprocedure and the associated timing based pacing procedure. In step 82the atrial fibrillation detection unit verifies the presence of atrialfibrillation. If fibrillation is detected, the supervised training taskis turned off, the neural network synaptic weights are frozen, and theassociated VA interval is obtained in step 84 from the TPDNN as given inEq. 3 above, is obtained beat after beat with the hemodynamic sensorinput used as reference. The associated VA interval replaces the sensedatrial event for synchronizing the bi-ventricular pacing feedback taskperformed by the adaptive CRT device described in FIG. 2 to whichreference is again made. If fibrillation is not detected, pacingcontinues according to the sensed atrial event during normal sinusrhythm in step 86. In this manner the CRT task continues undisturbedduring an atrial fibrillation episode. The bi-ventricular pacingfeedback task as explained in details in International Patentpublication WO 2005/007075, for example, can continue to resynchronisethe AV delay and W timings online during a fibrillation episode. In suchcase, the system uses the hemodynamic sensors temporal patterns asreference, being relative to the associated VA event that replaces thenormal sensed atrial event. When a normal atrial sinus rhythm isrecovered, the device controller is switched back to using the naturaldetected atrial signal and also the VA association supervised learningtask continues to learn to associate the hemodynamic sensor inputtemporal patterns into a VA intervals as explained above. It should bestressed that the two back and forth switching mechanisms describedabove, i.e. the adaptive CRT vs. non adaptive CRT operational modes andthe sensed atrial event vs. associated VA interval operational modes areindependent of each other.

In the case of atrial fibrillation which is conducted to the ventricle,the hemodynamic performance of the ventricle will also be disturbedsignificantly and the association of the VA interval can fail. In suchcase the ventricular pacing relating to the associated VA interval isstopped and anti-tachycardia pacing (ATP) or a defibrillation shockmight be the only effective therapy that can recover normal heart rhythmand function.

In FIG. 4 to which reference is now made, a state diagram relating toanother preferred embodiment of the present invention is described. Inaccordance with this embodiment the control system switches back andforth between two states. One state uses associated VA interval insteadof the sensed atrial event, also with a normal sinus rate. The otherstate uses the sensed atrial event. The system initializes with senseoperation mode, 90, after the convergence of the VA association task 92it switches to operate according to the associated VA interval, and thesensed VA interval is used thereafter only for on-line supervisedlearning while the associated VA interval value is used for the timingcycles of the micro-controller replacing the sensed atrial event inoperation mode 94. When a failure takes place, such as a large deviationof the associated VA interval from the sensed atrial event or any otherpre-defined failure, the system switches back, in transition 96, to usethe sensed atrial event.

There are two reasons to prefer working with the associated operationmode. First, the learned VA intervals association according tohemodynamic sensor temporal patterns reflects closely the hemodynamiccycle, i.e. the diastolic and systolic cycles and even more specificallythe passive and active filling times, the iso-volumetric contraction andthe ejection phase. Therefore, the associated operation mode can be amore accurate indication for cardiac cycle timing associated withimplanted pacemaker systems then the local sensed atrial IEGM signal. Itis assumed therefore that by using the associated VA interval accordingto hemodynamic sensor temporal patterns, better pacing can be achievedwith better AV synchrony to produce better clinical results as comparedto synchronizing based on the sensed IEGM atrial lead. Second, when theprediction error is smaller than the accumulated errors due to all noisesources in the system, such as possible lead movements and signaldegradation, sampling and digitization errors, and any other noise inthe implanted electronic circuits, the learned associated atrial eventis closer to the underlying physiological system timings and can correctthe accumulated error.

Synaptic Stability—Plasticity Dilemma

What are the steady state optimal values of the synaptic weights thatprovide the best performance? It depends on the neural networkarchitecture and also on environmental variables that might change withtime. Hence the optimal synaptic weights are functional of time andenvironment states and when the environment is dynamic, such as theheart muscle, the neural network architecture must be flexible enough toadapt its synaptic weights accordingly to ensure best performance.Therefore, in accordance with the present invention the steady statevalue of the synaptic weight are changeable with time with the presentapplication. In his above cited book, Stamatios V. Kartalopoulos, inpage 59, discusses the notion of elasticity of the learning system. Itis discussed how a learning system can be designed to remain plastic oradaptable enough to learn new things whenever they appear and yet remainstable enough to retain previously learned knowledge.

With the neural network temporal pattern recognition architecturepresented here each synapse change its learning rate parameter, η,according to its local activation state and a value of the membershipfunction calculated for each I&F neuron, i.e. the hit count rate. Whenthe dynamic synapse is highly activated in Hebb state and the hit countrate is high, the learning rate, η, is decreased and hence synapse gainsselective stability. The mechanism for adjusting selectively thelearning rate parameter brings stability and reduces the occurrence oflosing previously learned patterns by new input patterns. A neuralnetwork system employing a smaller learning rate parameter becomes aslow learner but still maintains a reduced plasticity. New patterns thatexcite other synapses in the middle layer will be processed with higherplasticity according to the selective stabilization mechanism describedhere. The selective stabilization process is improved further by addinga global small leakage component to all synapse excitation that cause aloss of activation with a constant slow rate. This mechanism affectssynapses that were previously highly activated but later became inactivefor a long period of time, to lose slowly their activation level andthus to increase their plasticity through acquiring a larger learningrate coefficient.

In accordance with the present invention, the spiking neural networkarchitecture strategy is to change for each synapse the learning ratecoefficient locally and selectively. Synapses that were active and causehits at the target time and hence a saturation of the hit count ratemembership function gain stability. The neural network architecturedescribed above demonstrates both plasticity and stability, and is morestable with regards to noise and erroneous temporal patterns, yet it isstill a fast learner of new temporal patterns due to the selectivity ofthe learned learning rate coefficients.

Advantages of Implementing the Invention

Applying the method of the present invention, the ventricular pacing issynchronized with the cardiac cycle timings even during atrialfibrillation and hence can unload hemodynamic stress and may furthersuppress the arrhythmia, according to argumentation of Taggart andSutton quoted above. It is expected to be clinically beneficial for bothcongestive heart failure (CHF) patients treated with CRT devices and forpatients treated with dual chamber pacemakers and ICD's.

What is claimed is:
 1. An adaptive dual chamber pacemaker and/orcardioverter defibrillator for delivering ventricular stimulation to theheart correlated with hemodynamic performance of the heart, comprising:a. at least one hemodynamic sensor for monitoring said hemodynamicperformance of the heart; b. an atrial electrode and at least oneventricular electrode, adapted to sense ventricular and atrial signals;c. a learning module comprising a spiking neural network processoradapted to learn to associate the ventricular-atrial intervals sensed bysaid electrodes with the hemodynamic performance sensed by said at leastone hemodynamic sensor, and calculate ventricular-atrial intervals andreplace said ventricular-atrial intervals calculated from the sensedventricular and atrial signals with the learned associatedventricular-atrial intervals and cause delivery according to the learnedassociated ventricular-atrial intervals of a ventricular stimulation tothe heart during atrial fibrillation episodes, wherein said spikingneural network processor further comprises: I. an input layer temporalsynchronizer performing a pre-processing stage of said hemodynamicperformance of the heart sensed by said at least one hemodynamic sensor;II. a middle layer comprising of an array of dynamic synapses, whereinsaid dynamic synapses are grouped in columns, and III. an output layercomprising at least one integrate and fire neuron for each said dynamicsynapse column of the middle layer, said at least one integrate and fireneuron being adapted to perform said learning to associateventricular-atrial intervals with said hemodynamic performance of theheart; d. a micro controller for controlling said learning module, ande. at least one pulse generator and operational amplifier controlled bysaid micro controller for stimulating the heart with said ventricularstimulation.
 2. A device according to claim 1 wherein the synapses ofthe middle layer of said spiking neural network processor have alearning rate parameter, and said synapses are self-trained applying aHebbian learning rule and said synapses locally and selectively changethe learning rate parameter in each synapse module according to a valueof a hit count rate membership function calculated by a post synapticintegrate and fire neuron according to an activation state of a synapsesuch that a synapse decreases its learning rate after said synapse wasactive and caused a saturation of said hit count rate membershipfunction of an output layer integrate and fire neuron.
 3. A deviceaccording to claim 1, wherein each said synapse is adapted to adjust alocal and selective learning rate parameter separately in order toimprove stability and preserve plasticity of said spiking neural networkprocessor.
 4. A device according to claim 1, said device learns toassociate said ventricular-atrial interval based on said hemodynamicperformance of the heart wherein the associated ventricular-atrialinterval replaces a sensed atrial event signal during atrialfibrillation episodes, wherein said device delivers said ventricularstimulation to the heart with dynamic atrio-ventricular delay whereinsaid stimulation is adapted continuously to the hemodynamic performanceof the heart.
 5. A device according to claim 1, wherein said spikingneural network is adapted to be implementable in a processor operatingwith low clock frequency at the range 1-10 KHz and with synaptic weightadaptation deriving reference from an input of said hemodynamic sensorand implementing a Hebbian learning rule.
 6. A device as in claim 1,adapted to determine the validity of the ventricular-atrial intervalassociated with the spiking neural network processor, and to determinewhether a ventricular-atrial interval association is poor or whether anysystem failure has occurred, wherein the associated ventricular-atrialinterval replaces a sensed atrial event signal as long as theventricular-atrial interval associated with said spiking neural networkis valid and where said cardiac resynchronization system is adapted toswitch back to working with said sensed event when theventricular-atrial interval association is poor or any failure occurs,said device being adapted to: (a) sense an atrial operation mode and anassociated ventricular-atrial interval operation mode, (b) perform atemporal pattern recognition of the spiking neural network and (c)switch back and forth between said sensed atrial operation mode and theassociated ventricular-atrial interval operation mode based upon saidtemporal pattern recognition of the spiking neural network; whereby saiddevice is implementable as an adaptive dual chamber pacemaker and/orintracardiac cardioverter defibrillator.
 7. A device as in claim 1wherein said ventricular-atrial interval sensed by said electrodes isadapted so that it can be sensed online.